fingerprint.regression: Regress trait evolution against trait ecology (following Cavender-Bares et al. 2004)

Description

Calculates traits' phylogenetic inertia and regresses this against trait similarity among co-existing species (sensu Cavender-Bares et al. 2004 Figure 6)

Usage

fingerprint.regression(data, eco.rnd = c("taxa.labels", "richness",
  "frequency", "sample.pool", "phylogeny.pool", "independentswap", "trialswap"),
  eco.method = c("quantile", "lm", "mantel"), eco.permute = 1000,
  evo.method = c("lambda", "delta", "kappa", "blom.k"), eco.swap = 1000,
  abundance = TRUE, ...)
## S3 method for class 'fingerprint.regression':
print(x, ...)
## S3 method for class 'fingerprint.regression':
summary(object, ...)
## S3 method for class 'fingerprint.regression':
plot(x, eco = c("slope", "corrected"),
  xlab = "Community Trait Similarity", ylab = "Phylogenetic inertia", ...)

Arguments

data

comparative.comm for analysis

eco.rnd

null distribution with which to compare your community data, one of: taxa.labels (DEFAULT), richness, frequency, sample.pool, phylogeny.pool, independentswap, trialswap

eco.permute

number of permutations for ecological null model (eco.rnd); default 1000

eco.method

how to compare distance matrices (only the lower triangle;), one of: lm (linear regression), quantile (DEFAULT; rq), mantel (

evo.method

how to measure phylogenetic inertia, one of: lambda (default), delta, kappa, blom.k; see phy.signal.

eco.swap

number of independent swap iterations to perform (if specified in eco.rnd; DEFAULT 1000)

abundance

whether to incorporate species' abundances (default: TRUE)

...

additional parameters to pass on to model fitting functions and plotting functions

fingerprint.regression object

object

fingerprint.regression object

eco

plot the observed slopes (DEFAULT: slope), or the median difference between the simulations and the observed values (corrected)

xlab

label for x-axis (default "Ecological Trait Coexistence")

ylab

label for y-axis (default "Phylogenetic inertia")

Details

While the term `fingerprint regression' is new to pez, the method is very similar to that employed in Cavender-Bares et al. 2004 Figure 6. For each trait, the phylogenetic inertia of speciestraits is regressed against their co-occurrence in the community matrix. Note that Pagel's $$\lambda$$, $$\delta$$, and $$\kappa$$ are used, unlike the original where a mantel test was employed. Moreover, note also that Pianka's distance (as described in the manuscript) is used to measure species overlap.

References

Cavender-Bares J., Ackerly D.D., Baum D.A. & Bazzaz F.A. (2004) Phylogenetic overdispersion in Floridian oak communities. The Americant Naturalist 163(6): 823--843.

Kembel, S.W., Cowan, P.D., Helmus, M.R., Cornwell, W.K., Morlon, H., Ackerly, D.D., Blomberg, S.P. & Webb, C.O. Picante: R tools for integrating phylogenies and ecology. Bioinformatics 26(11): 1463--1464.

Pagel M. Inferring the historical patterns of biological evolution. Nature 401(6756): 877--884.

Examples

Run this code

data(laja)
data <- comparative.comm(invert.tree, river.sites, invert.traits, river.env)
fingerprint.regression(data, eco.permute=10)
plot(fingerprint.regression(data, permute=10, method="lm"))